class_docstr: A simple subgraph loader that, given a pre-partioned :obj:`data` object,
  generates subgraphs from mini-batches in :obj:`ptr` (including their 1-hop
  neighbors).
  In contrast to :class:`SubgraphLoader`, this loader does not generate
  subgraphs from randomly sampled mini-batches, and should therefore only be
  used for evaluation.
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code: def __init__(
      self,
      data: Data,
      ptr: Tensor,
      batch_size: int = 1,
      bipartite: bool = True,
      log: bool = True,
      **kwargs,
  ):
      ptr = ptr[::batch_size]
      if int(ptr[-1]) != data.num_nodes:
          ptr = torch.cat([ptr, torch.tensor([data.num_nodes])], dim=0)
  
      super().__init__(
          data=data,
          ptr=ptr,
          batch_size=1,
          bipartite=bipartite,
          log=log,
          shuffle=False,
          num_workers=0,
          **kwargs,
      )
  
file_path: DIG/dig/lsgraph/method/GraphFMOB/loader.py
imports: from typing import NamedTuple, List, Tuple
  import time
  import torch
  from torch import Tensor
  from torch.utils.data import DataLoader
  from torch_sparse import SparseTensor
  from torch_geometric.data import Data
  from dig_ext.relabel import relabel_one_hop
label_desc: Use image node_id 1 to create a new EvalSubgraphLoader object from inherited base classes: SubgraphLoader with example: obj = EvalSubgraphLoader(data, ptr, batch_size, bipartite, log)